LEARNING PATHS

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RAG Applications

LangChain Pinecone OpenAI
Beginner Friendly

Retrieval Augmented Generation (RAG) is a powerful approach that enhances large language models by retrieving relevant information from external knowledge sources. In this track, you'll learn to build AI applications that can retrieve and reason over your own data, creating chatbots and assistants that answer questions based on your documents, websites, and knowledge bases.

What You'll Learn:

  • Vector databases and embeddings for semantic search
  • Document processing, chunking, and indexing strategies
  • Building conversational interfaces with context management
  • Deploying RAG applications to production environments
HANDS-ON PROJECTS

Featured Projects in this Track

Intermediate

Document Q&A Chatbot

Build a chatbot that can answer questions based on your PDF documents, using vector search and LLMs.

Python LangChain OpenAI
Advanced

Website Knowledge Base

Create a chatbot that can answer questions about any website by crawling and indexing its content.

Python LangChain OpenAI
Intermediate

Multi-source RAG Assistant

Build an advanced assistant that can retrieve information from multiple data sources including databases, APIs, and documents.

LangChain Chroma OpenAI
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Project-Based Learning
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Expert Mentorship